Recognizing Cattle Behaviours by Spatio-Temporal Reasoning Between Key Body Parts and Environmental Context

The accurate recognition of cattle behaviours is crucial for improving animal welfare and production efficiency in precision livestock farming. However, existing methods pay limited attention to recognising behaviours under occlusion or those involving subtle interactions between cattle and environmental objects in group farming scenarios. To address this limitation, we propose a novel spatio-temporal feature extraction network that explicitly models the associative relationships between key body parts of cattle and environmental factors, thereby enabling precise behaviour recognition. Specifically, the proposed approach first employs a spatio-temporal perception network to extract discriminative motion features of key body parts. Subsequently, a spatio-temporal relation integration module with metric learning is introduced to adaptively quantify the association strength between cattle features and environmental elements. Finally, a spatio-temporal enhancement network is utilised to further optimise the learned interaction representations. Experimental results on a public cattle behaviour dataset demonstrate that our method achieves a state-of-the-art mean average precision (mAP) of 87.19%, outperforming the advanced SlowFast model by 6.01 percentage points. Ablation studies further confirm the synergistic effectiveness of each module, particularly in recognising behaviours that rely on environmental interactions, such as drinking and grooming. This study provides a practical and reliable solution for intelligent cattle behaviour monitoring and highlights the significance of relational reasoning in understanding animal behaviours within complex environments.